cnn

cnn code

Importing the Keras libraries and packages

from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense

Initialising the CNN

classifier = Sequential()

Step 1 - Convolution

classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

Step 2 - Pooling

classifier.add(MaxPooling2D(pool_size = (2, 2)))

Adding a second convolutional layer

classifier.add(Conv2D(32, (3, 3), activation = 'relu')) classifier.add(MaxPooling2D(pool_size = (2, 2)))

Step 3 - Flattening

classifier.add(Flatten())

Step 4 - Full connection

classifier.add(Dense(units = 128, activation = 'relu')) classifier.add(Dense(units = 1, activation = 'sigmoid'))

Compiling the CNN

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')

classifier.fit_generator(training_set, steps_per_epoch = 8000, epochs = 25, validation_data = test_set, validation_steps = 2000)

#hw import numpy as np from keras.preprocessing import image test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64)) test_image = image.img_to_array(test_image) test_image = np.expand_dims(test_image, axis = 0) result = classifier.predict(test_image) training_set.class_indices if result[0][0] == 1: prediction = 'dog' else: prediction = 'cat'